Quick Start
Get up and running with DeepNode in under 5 minutes. This guide walks you through installation, authentication, and deploying your first model.
Prerequisites
- Python 3.9 or later
- A DeepNode account (request access)
- A trained model (any major ML framework)
1. Install the CLI
$ pip install deepnode-cli
2. Authenticate
$ deepnode auth login
This opens your browser for OAuth authentication. Your API key is stored locally in ~/.deepnode/credentials.
3. Deploy a model
$ deepnode deploy --model ./my_model --gpu nova --replicas 3
DeepNode automatically detects your framework, builds a container, and deploys to the nearest region. You'll receive an inference endpoint within minutes.
Installation
pip (recommended)
$ pip install deepnode-cli
Shell script (macOS / Linux)
$ curl -fsSL https://get.deepnode.cc | sh
Container
$ deepnode pull cli:latest
$ deepnode run cli --version
Verify installation
$ deepnode --version
deepnode-cli 0.14.2
CLI Reference
The DeepNode CLI provides a complete interface for managing your infrastructure from the terminal.
Global flags
--project <id> Override the active project
--region <region> Target region (eu-west, us-east, ap-south)
--output <format> Output format: table, json, yaml
--verbose Enable debug logging
deepnode deploy
Deploy a model to production infrastructure.
$ deepnode deploy --model <path> [options]
Options:
--gpu <type> GPU type: spark, pulse, nova, apex (default: pulse)
--replicas <n> Number of replicas (default: 1)
--min-replicas <n> Minimum replicas for autoscaling
--max-replicas <n> Maximum replicas for autoscaling
--region <region> Deployment region
--env <key=val> Environment variables (repeatable)
deepnode train
Launch a distributed training job.
$ deepnode train --config <file> [options]
Options:
--cluster <name> Target cluster
--nodes <n> Number of nodes (default: 1)
--gpu-per-node <n> GPUs per node (default: all available)
--priority <level> Job priority: low, normal, high
--tag <name> Tag for experiment tracking
deepnode serve
Serve a registered model version.
$ deepnode serve --model <name:version> [options]
Options:
--autoscale Enable autoscaling
--timeout <ms> Request timeout (default: 30000)
--batch-size <n> Maximum batch size for inference
deepnode status
View status of deployments and training jobs.
$ deepnode status [deployment-id]
$ deepnode status --all
deepnode logs
Stream logs from a deployment or training job.
$ deepnode logs <deployment-id> --follow --tail 100
API Reference
The DeepNode REST API is available at https://api.deepnode.cc/v1. All requests require an API key passed via the Authorization header.
Authentication
Authorization: Bearer dn_live_xxxxxxxxxxxxxxxxxxxx
List deployments
GET /v1/deployments
Response:
{
"deployments": [
{
"id": "dep_abc123",
"model": "my-model",
"version": "2.4.1",
"status": "running",
"region": "eu-west",
"replicas": 3,
"endpoint": "https://my-model.serve.deepnode.cc"
}
]
}
Create deployment
POST /v1/deployments
Body:
{
"model": "my-model:2.4.1",
"gpu": "nova",
"replicas": 3,
"region": "eu-west",
"autoscale": {
"min": 2,
"max": 10,
"target_gpu_utilization": 0.7
}
}
Run inference
POST /v1/inference/<deployment-id>
Body:
{
"inputs": { ... }
}
Response:
{
"outputs": { ... },
"latency_ms": 42,
"model_version": "2.4.1"
}
Get metrics
GET /v1/deployments/<id>/metrics?period=24h
Response:
{
"requests": 145230,
"avg_latency_ms": 47,
"p99_latency_ms": 89,
"gpu_utilization": 0.68,
"error_rate": 0.001
}
Deployment
Regions
DeepNode is available in the following regions:
- eu-west — Northern Europe
- eu-central — Frankfurt, Germany
- us-east — Virginia, USA
- us-west — Oregon, USA
- ap-south — Singapore
GPU types
- Spark — Budget-friendly inference (16 GB VRAM)
- Pulse — Balanced training/inference (24 GB VRAM)
- Nova — High-performance training (80 GB VRAM)
- Apex — Maximum throughput (80 GB HBM3) — Enterprise only
Autoscaling
Enable autoscaling to automatically adjust replicas based on GPU utilization or request queue depth.
$ deepnode deploy --model ./my_model \
--gpu nova \
--min-replicas 2 \
--max-replicas 10 \
--autoscale
Environment variables
Pass environment variables to your deployment:
$ deepnode deploy --model ./my_model \
--env MODEL_PRECISION=fp16 \
--env MAX_BATCH_SIZE=32
FAQ
What frameworks are supported?
DeepNode supports all major ML frameworks. Framework detection is automatic — just point us at your model directory.
How does billing work during the beta?
During the public beta, Starter tier is completely free (100 GPU hours/month). Pro and Enterprise plans are billed monthly based on usage. See pricing for details.
Can I use my own container images?
Yes. While DeepNode auto-detects frameworks and builds containers for you, you can also bring a custom container image. Specify it with --image during deployment.
Is my data encrypted?
All data is encrypted at rest (AES-256) and in transit (TLS 1.3). Enterprise customers can additionally enable VPC peering for private network connectivity.
How do I get support?
Starter users get community support via our forum. Pro users get priority email support. Enterprise customers receive 24/7 dedicated support with a guaranteed SLA. Reach us at hello@deepnode.cc.
Where is DeepNode hosted?
Our primary infrastructure runs on Tier III+ data centers across Europe and North America.